About

Concept

Archives of Data Science, Series B (Data Sets, Algorithms, Processes, and Services)
addresses the problems of data science.
The journal covers scientific articles which improve
methods, algorithms, and processes over the whole data life cycle.
In addition, papers on data analysis processes, services, and (scientific)
infrastructures are welcome. Prototypes of processes and services
should be available on the web site of the journal.
Examples of such processes are benchmarks, resampling processes, …

The journal is organized around data sets and it
follows the traditional structure of volumes and numbers,
however with a non-traditional interpretation of what constitutes a number
and a volume.
A number is a an open ended stream of articles which starts with a seminal
article on a data set (head article) and continues with articles which propose
innovative ways of “handling” the data set (tail articles).

The organization of the journal requires that a number always starts with an article about
a data set, followed by papers with methods applied to the data set in the head article.
A purely theoretic paper (without reference and application to a head article) is not suitable
for the Archives of Data Science, Series B. The editors reserve the right to delegate
such articles to other Data Science journals.

All publications are available both as free OpenAccess articles as well as printed version
orderable
via
KIT Scientific Publishing (KSP).

Contributions

Head Article

Describes a data set and provides access to it. All data sets must be open.
Such a seminal article must provide
at least the structure of the data set and the interfaces available
to access it. Data sets are also defined in a wide sense:
E.g. comma-separated files, relational data-bases, open linked data,
data-harvesting processes, data generators, and interfaces to data streams.
In addition, the measurement process for the
data set must be explained in detail, restrictions and possible problems
of the measurement process should be covered. The authors of such a seminal
article are also expected to provide problems and questions
(ideally a challenge) that they would like to see solved
by analyzing the data set.

Tail Articles

They describe innovative ways of
generating, accessing, storing, distributing, analyzing, visualizing,
and using data in the broadest sense. In addition, each article must
be complemented by a well-documented open source version of a software
package which implements the methods described in the tail article.

Peer Review Policy

Every submitted paper is reviewed by at least two reviewers.

Accepted final papers will be published as fully reviewed online-first version that are freely
available and already citable with the note Online-First in the reference. Final papers are
published in cooperation with
KIT Scientific Publishing
(KSP)
as an electronic version. Each issue of the journal can also be
ordered
as print-on-demand version.

Copyright

Copyright for articles published in this journal is retained by the authors, with first publication
rights granted to the journal. By virtue of their appearance in this open access journal, articles are
free to use, with proper attribution, in educational and other non-commercial or commercial settings.
By submitting an article the authors agree that their work is published (on acceptance) under the terms of the
Creative Commons Attribution-ShareAlike (CC CY-SA) license.
For details, we refer to the
FAQ of KIT Scientific Publishing (in
German).
For further information on this license, see:
http://creativecommons.org/licenses/by-sa/4.0/